A Field Experiment on Labor Market Discrimination

A recent paper by Marianne Bertrand and Sendhil Mullainathan has been getting a lot of press in the shadow of the debate over the University of Michigan’s affirmative action policies. The paper, entitled Are Emily and Brendan More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination, is accessible to the non-specialist and well worth reading.

The authors started with a collection of resumes that were downloaded from web sites catering to jobseekers, and modified them to remove identifying information. They then sent out resumes in response to vacancies advertised in Boston and Chicago, after first randomly assigning to each resume either a "white-sounding" or a "black-sounding" name. The names were obtained from historical birth records, and were based on the relative frequencies of names in black and white households. Hence names that were either much more common in white households than in black ones (such as Meredith and Todd) or much more common in black households than in white ones (such as Tanisha and Hakim) were selected for the study. Names such as Michael and Vanessa, which are common in both communities and hence carry little or no racial association, were not used. The authors found that resumes which had been randomly assigned white-sounding names elicited significantly higher callback rates than those assigned black-sounding names.

Many economists will be tempted to interpret these findings through the lens of statistical discrimination theory: if names carry information about the populations from which subjects are drawn, and these populations differ with respect to their productive characteristics, then names will carry statistical information about worker productivity. The authors try to test this interpretation by selectively improving some resumes prior to the random assignment of names. This is done, for instance, by filling in gaps in employment history and adding volunteer work. They find that such improvements significantly increase callback rates for resumes assigned white-sounding names but have negligible effects for those assigned black-sounding names. This flies in the face of the standard statistical discrimination model, in which greater information about candidates should narrow rather than widen discriminatory treatment.

So what’s going on? The authors are careful to leave open the possibility that more sophisticated models of statistical discrimination can account for their findings. But sometimes the simplest explanation really is the right one, and in my own view this is the case here. It seems to me that there is a subset of employers who have a strong negative gut-reaction to a black-sounding name and don’t bother to scan resumes for additional information once this reaction is triggered. The number of such individuals may be small relative to the population of employers, but they must be sufficiently numerous for their behavior to result in statistically discernible aggregate effects.

To see just how much publicity this article has generated, try a google search for black-sounding names, where you'll find links to the Chicago Sun Times, Boston Globe, New York Newsday, and the New York Post among dozens of media outlets.

The Turing Tournament

Two very interesting papers on learning in experimental games are posted on the Turing Tournament webste at Caltech. One is an experimental paper by Arifovic, McKelvey and Pevnitskaya, which focuses on the ability of standard learning models to account for the behavior of human subjects in selected finitely repeated games. There are some striking patterns in the human data that standard learning models consistently fail to replicate, such as alternation between the two pure strategy equilibria in the repeated battle-of-the-sexes, and significant cooperation in the repeated Prisoners' Dilemma. The companion paper is by McKelvey and Palfrey, and calls for the development of "strategic learning" models, which allow for the learning not just of stage-game actions but also of repeated game strategies. The Turing Tournament itself is a fascinating attempt to elicit the development of better learning models and I hope that the tragic and untimely death of Richard McKelvey doesn't derail the project.

Glenn Loury's Du Bois Lectures

For anyone interested in Race in the United States, and in social division more generally, Glenn Loury's recent book The Anatomy of Racial Inequality is essential reading. The book is based on his Du Bois Lectures, delivered in 2000 at Harvard, and goes well beyond his earlier work on statistical discrimination and self-fulfilling negative stereotypes. He does this by stepping across traditional disciplinary boundaries and addressing issues such as the salience of racial markers and the persistence of racal stigma. Unlike discrimination, which "is about how people are treated", stigma "is about who, at the deepest cognitive level, they are understood to be". Loury argues that racial egalitarianism is a legitimate goal of public policy in the historical context of the United States, and that this objective may "properly" be pursued by using methods such as affirmative action, which violate the procedural principle of race-blindness. But this book is about much more than affirmative action, and breaks new ground in the national dialogue on race.

A Blog for Economists?

A blog for economists? Inspired by Rakesh Vohra's must-read list and the NAJ idea, here's a source for some literature that may interest economists and other social scientists. Coverage is very selective and based on my current research interests.